Format

Send to

Choose Destination
Hum Brain Mapp. 2019 Jun 1;40(8):2546-2555. doi: 10.1002/hbm.24543. Epub 2019 Feb 21.

Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers.

Author information

1
Translational and Molecular Imaging Institute, Icahn School of Medicine, Mount Sinai Medical Center, New York, New York.
2
Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.
3
Sagol Brain Institute Tel-Aviv Medical Center, Tel-Aviv, Israel.
4
Movement Disorders Unit, Neurological Institute, Tel-Aviv Medical Center, Tel-Aviv, Israel.
5
Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
6
Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.
7
Department of Neurology and Parkinson Centre, Radboud University Medical Centre, Nijmegen, The Netherlands.
8
Tel-Aviv Medical Center, Genetic Institute, Tel-Aviv, Israel.
9
Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel-Aviv Medical Center, Tel-Aviv, Israel.

Abstract

Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an "at risk" group for future development of Parkinson's disease (PD) and have demonstrated task related fMRI changes. However, resting-state networks have received less research focus, thus this study aimed to assess the integrity of the motor, default mode (DMN), salience (SAL), and dorsal attention (DAN) networks among this unique population by using two different connectivity measures: interregional functional connectivity analysis and Dependency network analysis (DEP NA). Machine learning classification methods were used to distinguish connectivity between the two groups of participants. Forty-four NMC and 41 non-manifesting non-carriers (NMNC) participated in this study; while no behavioral differences on standard questionnaires could be detected, NMC demonstrated lower connectivity measures in the DMN, SAL, and DAN compared to NMNC but not in the motor network. Significant correlations between NMC connectivity measures in the SAL and attention were identified. Machine learning classification separated NMC from NMNC with an accuracy rate above 0.8. Reduced integrity of non-motor networks was detected among NMC of the G2019S mutation in the LRRK2 gene prior to identifiable changes in connectivity of the motor network, indicating significant non-motor cerebral changes among populations "at risk" for future development of PD.

KEYWORDS:

LRRK2; Parkinson's disease; graph theory network analysis; machine learning classification; resting state fMRI

PMID:
30793410
DOI:
10.1002/hbm.24543

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center